Digital image capture technologies have seen widespread use in consumer devices. Utilizing Optical Character Recognition (OCR), such technologies are capable of recognizing certain limited content, such as the presence of text characters, in the digitally captured images. However, current technologies do not offer users the flexibility to manipulate or post-edit the recognized content in a manner customized to the content of the image.
For example, if a document such as an itemized store receipt is digitally captured, current OCR software may identify text on the receipt, but may not automatically provide the user with capabilities to manipulate text items, re-calculate sales tax and price information based on the manipulation, or otherwise perform receipt-specific functions that would be convenient to the user. Similarly, other types of captured images such as restaurant menus, fillable forms, etc., may contain content-specific information that is not easily manipulated or post-edited using current technologies.
Accordingly, it would be desirable to provide novel and efficient techniques to assign context to an image based on the image content, and to provide flexible and context-specific options for a user to manipulate recognized data fields in the image.